منابع مشابه
System Identification Based on Frequency Response Noisy Data
In this paper, a new algorithm for system identification based on frequency response is presented. In this method, given a set of magnitudes and phases of the system transfer function in a set of discrete frequencies, a system of linear equations is derived which has a unique and exact solution for the coefficients of the transfer function provided that the data is noise-free and the degrees of...
متن کاملSystem Identification Based on Frequency Response Noisy Data
In this paper, a new algorithm for system identification based on frequency response is presented. In this method, given a set of magnitudes and phases of the system transfer function in a set of discrete frequencies, a system of linear equations is derived which has a unique and exact solution for the coefficients of the transfer function provided that the data is noise-free and the degrees of...
متن کاملsystem identification based on frequency response noisy data
in this paper, a new algorithm for system identification based on frequency response is presented. in this method, given a set of magnitudes and phases of the system transfer function in a set of discrete frequencies, a system of linear equations is derived which has a unique and exact solution for the coefficients of the transfer function provided that the data is noise-free and the degrees of...
متن کاملContinuous Data Assimilation with Stochastically Noisy Data
We analyze the performance of a data-assimilation algorithm based on a linear feedback control when used with observational data that contains measurement errors. Our model problem consists of dynamics governed by the two-dimension incompressible Navier–Stokes equations, observational measurements given by finite volume elements or nodal points of the velocity field and measurement errors which...
متن کامل1 Minimization with Noisy Data
Compressed sensing aims at recovering a sparse signal x ∈ RN from few nonadaptive, linear measurements Φ(x) given by a measurement matrix Φ. One of the fundamental recovery algorithms is an `1 minimisation. In this paper we investigate the situation when our measurement Φ(x) is contaminated by arbitrary noise under the assumption that the matrix Φ satisfies the restricted isometry property. Thi...
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ژورنال
عنوان ژورنال: Applied Mathematics Letters
سال: 1995
ISSN: 0893-9659
DOI: 10.1016/0893-9659(95)00090-d